Overview

Dataset statistics

Number of variables22
Number of observations41533
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory176.0 B

Variable types

NUM11
BOOL6
CAT5

Warnings

city_name has a high cardinality: 1047 distinct values High cardinality
region is highly correlated with provinceHigh correlation
province is highly correlated with regionHigh correlation
house_area is highly skewed (γ1 = 121.9365874) Skewed
garden_area is highly skewed (γ1 = 108.5190241) Skewed
surface_of_the_land is highly skewed (γ1 = 53.81642955) Skewed
Unnamed: 0 has unique values Unique
terrace_area has 25193 (60.7%) zeros Zeros
garden_area has 33596 (80.9%) zeros Zeros
surface_of_the_land has 21205 (51.1%) zeros Zeros
number_of_facades has 10404 (25.0%) zeros Zeros

Reproduction

Analysis started2020-09-17 22:09:15.588396
Analysis finished2020-09-17 22:09:45.401536
Duration29.81 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct41533
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25731.71478
Minimum0
Maximum52075
Zeros1
Zeros (%)< 0.1%
Memory size324.5 KiB
2020-09-18T00:09:45.642536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2537.6
Q112558
median25529
Q338774
95-th percentile49447.4
Maximum52075
Range52075
Interquartile range (IQR)26216

Descriptive statistics

Standard deviation15100.07888
Coefficient of variation (CV)0.5868275399
Kurtosis-1.209303272
Mean25731.71478
Median Absolute Deviation (MAD)13126
Skewness0.02404907666
Sum1068715310
Variance228012382.2
MonotocityStrictly increasing
2020-09-18T00:09:45.822684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
506101< 0.1%
 
158211< 0.1%
 
137721< 0.1%
 
35311< 0.1%
 
14821< 0.1%
 
76251< 0.1%
 
55761< 0.1%
 
260541< 0.1%
 
301481< 0.1%
 
Other values (41523)41523> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
520751< 0.1%
 
520731< 0.1%
 
520721< 0.1%
 
520711< 0.1%
 
520701< 0.1%
 

postal_code
Real number (ℝ≥0)

Distinct1058
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5173.781475
Minimum1000
Maximum9992
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:46.002945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1080
Q12322
median4620
Q38380
95-th percentile9402
Maximum9992
Range8992
Interquartile range (IQR)6058

Descriptive statistics

Standard deviation2977.364009
Coefficient of variation (CV)0.5754715431
Kurtosis-1.511348912
Mean5173.781475
Median Absolute Deviation (MAD)2835
Skewness0.09705636893
Sum214882666
Variance8864696.443
MonotocityNot monotonic
2020-09-18T00:09:46.177256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
83007571.8%
 
84007051.7%
 
90006521.6%
 
11805191.2%
 
10004731.1%
 
83704521.1%
 
40004331.0%
 
86703670.9%
 
10503450.8%
 
20003360.8%
 
Other values (1048)3649487.9%
 
ValueCountFrequency (%) 
10004731.1%
 
10201390.3%
 
10303330.8%
 
10401530.4%
 
10503450.8%
 
ValueCountFrequency (%) 
99925< 0.1%
 
999114< 0.1%
 
9990490.1%
 
998810< 0.1%
 
99822< 0.1%
 

city_name
Categorical

HIGH CARDINALITY

Distinct1047
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
Antwerpen
 
929
Knokke
 
757
Oostende
 
705
Gent
 
652
Uccle
 
519
Other values (1042)
37971 
ValueCountFrequency (%) 
Antwerpen9292.2%
 
Knokke7571.8%
 
Oostende7051.7%
 
Gent6521.6%
 
Uccle5191.2%
 
Bruxelles4731.1%
 
Uitkerke4521.1%
 
Glain4331.0%
 
Wulpen3670.9%
 
Ixelles3450.8%
 
Other values (1037)3590186.4%
 
2020-09-18T00:09:46.631578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)0.2%
2020-09-18T00:09:46.810276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length8
Mean length8.570413888
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
0
22294 
1
19239 
ValueCountFrequency (%) 
02229453.7%
 
11923946.3%
 
2020-09-18T00:09:46.918185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
house
16596 
apartment
15251 
villa
2374 
duplex
 
1274
ground floor
 
1093
Other values (17)
4945 
ValueCountFrequency (%) 
house1659640.0%
 
apartment1525136.7%
 
villa23745.7%
 
duplex12743.1%
 
ground floor10932.6%
 
penthouse8232.0%
 
apartment block7241.7%
 
mixed use building6791.6%
 
mansion3930.9%
 
exceptional property3810.9%
 
Other values (12)19454.7%
 
2020-09-18T00:09:47.037267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T00:09:47.209958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length7
Mean length7.527147088
Min length3

price
Real number (ℝ≥0)

Distinct3535
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315395.0649
Minimum2500
Maximum950000
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:47.382498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile120000
Q1199000
median275000
Q3380000
95-th percentile689000
Maximum950000
Range947500
Interquartile range (IQR)181000

Descriptive statistics

Standard deviation169480.0016
Coefficient of variation (CV)0.5373578107
Kurtosis1.884328684
Mean315395.0649
Median Absolute Deviation (MAD)85000
Skewness1.362081539
Sum1.309930323e+10
Variance2.872347093e+10
MonotocityNot monotonic
2020-09-18T00:09:47.563406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2490005701.4%
 
1990005621.4%
 
2990005601.3%
 
2950005401.3%
 
2250005341.3%
 
2750005271.3%
 
3250004391.1%
 
2350004281.0%
 
1750004281.0%
 
3950004241.0%
 
Other values (3525)3652187.9%
 
ValueCountFrequency (%) 
25003< 0.1%
 
66001< 0.1%
 
81601< 0.1%
 
99991< 0.1%
 
100004< 0.1%
 
ValueCountFrequency (%) 
950000760.2%
 
9490009< 0.1%
 
9480002< 0.1%
 
9470003< 0.1%
 
945000360.1%
 

number_of_rooms
Real number (ℝ≥0)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.848168926
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:47.724670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum30
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.360592664
Coefficient of variation (CV)0.4777078534
Kurtosis21.51819673
Mean2.848168926
Median Absolute Deviation (MAD)1
Skewness2.504196405
Sum118293
Variance1.851212396
MonotocityNot monotonic
2020-09-18T00:09:47.864661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
21406233.9%
 
31356332.7%
 
4593514.3%
 
1431410.4%
 
521485.2%
 
68882.1%
 
72780.7%
 
81440.3%
 
9620.1%
 
10570.1%
 
Other values (12)820.2%
 
ValueCountFrequency (%) 
1431410.4%
 
21406233.9%
 
31356332.7%
 
4593514.3%
 
521485.2%
 
ValueCountFrequency (%) 
302< 0.1%
 
242< 0.1%
 
231< 0.1%
 
221< 0.1%
 
202< 0.1%
 

house_area
Real number (ℝ≥0)

SKEWED

Distinct677
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.1154022
Minimum1
Maximum31700
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:48.035793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q192
median130
Q3187
95-th percentile330
Maximum31700
Range31699
Interquartile range (IQR)95

Descriptive statistics

Standard deviation184.0280121
Coefficient of variation (CV)1.186394191
Kurtosis20792.6004
Mean155.1154022
Median Absolute Deviation (MAD)43
Skewness121.9365874
Sum6442408
Variance33866.30925
MonotocityNot monotonic
2020-09-18T00:09:48.217995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
909062.2%
 
1208972.2%
 
1008882.1%
 
1508352.0%
 
1407681.8%
 
807501.8%
 
1107131.7%
 
2007111.7%
 
1606951.7%
 
1306721.6%
 
Other values (667)3369881.1%
 
ValueCountFrequency (%) 
14< 0.1%
 
53< 0.1%
 
111< 0.1%
 
132< 0.1%
 
142< 0.1%
 
ValueCountFrequency (%) 
317001< 0.1%
 
35601< 0.1%
 
24001< 0.1%
 
20191< 0.1%
 
17001< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
1
28961 
0
12572 
ValueCountFrequency (%) 
12896169.7%
 
01257230.3%
 
2020-09-18T00:09:48.348544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

open_fire
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
0
39340 
1
 
2193
ValueCountFrequency (%) 
03934094.7%
 
121935.3%
 
2020-09-18T00:09:48.402325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

terrace
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
1
25588 
0
15945 
ValueCountFrequency (%) 
12558861.6%
 
01594538.4%
 
2020-09-18T00:09:48.455490image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

terrace_area
Real number (ℝ≥0)

ZEROS

Distinct183
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.383381889
Minimum0
Maximum1150
Zeros25193
Zeros (%)60.7%
Memory size324.5 KiB
2020-09-18T00:09:48.566875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile42
Maximum1150
Range1150
Interquartile range (IQR)12

Descriptive statistics

Standard deviation22.41383944
Coefficient of variation (CV)2.388673903
Kurtosis392.0609731
Mean9.383381889
Median Absolute Deviation (MAD)0
Skewness12.43972243
Sum389720
Variance502.3801986
MonotocityNot monotonic
2020-09-18T00:09:48.752862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02519360.7%
 
1010022.4%
 
209592.3%
 
157821.9%
 
127361.8%
 
67061.7%
 
87011.7%
 
306541.6%
 
255831.4%
 
95551.3%
 
Other values (173)966223.3%
 
ValueCountFrequency (%) 
02519360.7%
 
1720.2%
 
22780.7%
 
33530.8%
 
45181.2%
 
ValueCountFrequency (%) 
11501< 0.1%
 
10201< 0.1%
 
7611< 0.1%
 
7081< 0.1%
 
6002< 0.1%
 

garden
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
0
28250 
1
13283 
ValueCountFrequency (%) 
02825068.0%
 
11328332.0%
 
2020-09-18T00:09:48.882576image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

garden_area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct1155
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.9679773
Minimum0
Maximum312600
Zeros33596
Zeros (%)80.9%
Memory size324.5 KiB
2020-09-18T00:09:48.997392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile500
Maximum312600
Range312600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1947.680351
Coefficient of variation (CV)14.32455193
Kurtosis16252.3201
Mean135.9679773
Median Absolute Deviation (MAD)0
Skewness108.5190241
Sum5647158
Variance3793458.75
MonotocityNot monotonic
2020-09-18T00:09:49.178339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03359680.9%
 
1002430.6%
 
2002140.5%
 
501670.4%
 
3001650.4%
 
1501560.4%
 
601350.3%
 
4001340.3%
 
5001240.3%
 
301230.3%
 
Other values (1145)647615.6%
 
ValueCountFrequency (%) 
03359680.9%
 
1610.1%
 
24< 0.1%
 
33< 0.1%
 
47< 0.1%
 
ValueCountFrequency (%) 
3126001< 0.1%
 
888001< 0.1%
 
850001< 0.1%
 
630001< 0.1%
 
580001< 0.1%
 

surface_of_the_land
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2963
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.4441047
Minimum0
Maximum400000
Zeros21205
Zeros (%)51.1%
Memory size324.5 KiB
2020-09-18T00:09:49.371749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3406
95-th percentile1811.4
Maximum400000
Range400000
Interquartile range (IQR)406

Descriptive statistics

Standard deviation3561.331513
Coefficient of variation (CV)6.626422137
Kurtosis4785.302185
Mean537.4441047
Median Absolute Deviation (MAD)0
Skewness53.81642955
Sum22321666
Variance12683082.14
MonotocityNot monotonic
2020-09-18T00:09:49.560863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02120551.1%
 
1501830.4%
 
2001700.4%
 
1001640.4%
 
2501540.4%
 
3001510.4%
 
10001460.4%
 
1201410.3%
 
4001220.3%
 
6001180.3%
 
Other values (2953)1897945.7%
 
ValueCountFrequency (%) 
02120551.1%
 
1230.1%
 
22< 0.1%
 
41< 0.1%
 
53< 0.1%
 
ValueCountFrequency (%) 
4000001< 0.1%
 
2647811< 0.1%
 
1203001< 0.1%
 
1200002< 0.1%
 
1178001< 0.1%
 

number_of_facades
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.064406616
Minimum0
Maximum4
Zeros10404
Zeros (%)25.0%
Memory size324.5 KiB
2020-09-18T00:09:49.728355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.40590375
Coefficient of variation (CV)0.6810207538
Kurtosis-1.089578788
Mean2.064406616
Median Absolute Deviation (MAD)1
Skewness-0.2304527708
Sum85741
Variance1.976565354
MonotocityNot monotonic
2020-09-18T00:09:49.853521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
21499536.1%
 
01040425.0%
 
4817119.7%
 
3755218.2%
 
14111.0%
 
ValueCountFrequency (%) 
01040425.0%
 
14111.0%
 
21499536.1%
 
3755218.2%
 
4817119.7%
 
ValueCountFrequency (%) 
4817119.7%
 
3755218.2%
 
21499536.1%
 
14111.0%
 
01040425.0%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
0
40829 
1
 
704
ValueCountFrequency (%) 
04082998.3%
 
17041.7%
 
2020-09-18T00:09:49.963168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
as new
12312 
good
11341 
None
10060 
to be done up
2916 
to renovate
2531 
Other values (2)
2373 
ValueCountFrequency (%) 
as new1231229.6%
 
good1134127.3%
 
None1006024.2%
 
to be done up29167.0%
 
to renovate25316.1%
 
just renovated22265.4%
 
to restore1470.4%
 
2020-09-18T00:09:50.125647image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T00:09:50.240953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:50.530344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length4
Mean length6.208532974
Min length4

lattitude
Real number (ℝ≥0)

Distinct1052
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.318006792
Minimum2.580669689
Maximum6.3009381
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:50.788352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.580669689
5-th percentile2.9203275
Q13.7463234
median4.3667216
Q34.849314652
95-th percentile5.622980506
Maximum6.3009381
Range3.720268411
Interquartile range (IQR)1.102991252

Descriptive statistics

Standard deviation0.8096614069
Coefficient of variation (CV)0.1875081365
Kurtosis-0.6412711046
Mean4.318006792
Median Absolute Deviation (MAD)0.5585481
Skewness-0.07791596416
Sum179339.7761
Variance0.6555515938
MonotocityNot monotonic
2020-09-18T00:09:50.987589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.39970819292.2%
 
3.3233738617571.8%
 
2.92032757051.7%
 
3.71415496521.6%
 
4.33723485191.2%
 
4.3516974731.1%
 
3.140486814521.1%
 
5.5418644331.0%
 
2.7073119163670.9%
 
4.38157073450.8%
 
Other values (1042)3590186.4%
 
ValueCountFrequency (%) 
2.5806696892380.6%
 
2.62625887< 0.1%
 
2.64344877440.1%
 
2.6449117152< 0.1%
 
2.6733218< 0.1%
 
ValueCountFrequency (%) 
6.30093812< 0.1%
 
6.26424981< 0.1%
 
6.2578278< 0.1%
 
6.20535735< 0.1%
 
6.18849323< 0.1%
 

longitude
Real number (ℝ≥0)

Distinct1052
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.85129752
Minimum49.5085018
Maximum51.4743516
Zeros0
Zeros (%)0.0%
Memory size324.5 KiB
2020-09-18T00:09:51.192343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum49.5085018
5-th percentile50.3102184
Q150.666357
median50.8695429
Q351.09779175
95-th percentile51.2996935
Maximum51.4743516
Range1.9658498
Interquartile range (IQR)0.43143475

Descriptive statistics

Standard deviation0.3259065571
Coefficient of variation (CV)0.006409011627
Kurtosis1.455878902
Mean50.85129752
Median Absolute Deviation (MAD)0.2213379
Skewness-0.9548716278
Sum2112006.94
Variance0.106215084
MonotocityNot monotonic
2020-09-18T00:09:51.384607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51.22110979292.2%
 
51.349429657571.8%
 
51.23031777051.7%
 
51.03971296521.6%
 
50.80182015191.2%
 
50.84655734731.1%
 
51.29969354521.1%
 
50.6482054331.0%
 
51.097791753670.9%
 
50.82228543450.8%
 
Other values (1042)3590186.4%
 
ValueCountFrequency (%) 
49.508501811< 0.1%
 
49.557756211< 0.1%
 
49.558079414< 0.1%
 
49.55819252< 0.1%
 
49.564206517< 0.1%
 
ValueCountFrequency (%) 
51.4743516340.1%
 
51.467795710< 0.1%
 
51.460924956< 0.1%
 
51.45063155290.1%
 
51.431558254< 0.1%
 

province
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
Flandre-Occidentale
7322 
Anvers
5455 
Flandre-Orientale
5204 
Hainaut
4287 
Liège
4133 
Other values (6)
15132 
ValueCountFrequency (%) 
Flandre-Occidentale732217.6%
 
Anvers545513.1%
 
Flandre-Orientale520412.5%
 
Hainaut428710.3%
 
Liège413310.0%
 
Bruxelles-Capitale40879.8%
 
Brabant flamand38459.3%
 
Limbourg26096.3%
 
Brabant wallon17964.3%
 
Namur16203.9%
 
2020-09-18T00:09:51.605717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T00:09:51.779540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length14
Mean length12.2335733
Min length5

region
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size324.5 KiB
Flandre
24435 
Wallonie
13011 
Bruxelles
4087 
ValueCountFrequency (%) 
Flandre2443558.8%
 
Wallonie1301131.3%
 
Bruxelles40879.8%
 
2020-09-18T00:09:52.019900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-18T00:09:52.238520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:52.386407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length7.510076325
Min length7

Interactions

2020-09-18T00:09:23.324243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:23.500947image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T00:09:23.864718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:24.041330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:24.213146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:24.386712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:24.591472image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:24.803068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:25.004402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:25.198123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T00:09:37.614951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T00:09:37.969145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T00:09:40.547614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-18T00:09:41.516729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:41.687893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:41.860672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.028261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.208213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.375260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.542985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.706630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:42.877270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.045291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.216969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.387044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.567835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.745016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:43.919805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-18T00:09:52.599065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-18T00:09:53.343128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-18T00:09:53.795634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-18T00:09:54.183832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-18T00:09:54.679214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-18T00:09:44.344119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-18T00:09:44.991126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Unnamed: 0postal_codecity_nametype_of_propertysubtype_of_propertypricenumber_of_roomshouse_areafully_equipped_kitchenopen_fireterraceterrace_areagardengarden_areasurface_of_the_landnumber_of_facadesswimming_poolstate_of_the_buildinglattitudelongitudeprovinceregion
001050Ixelles0house340000.062031010009520to be done up4.38157150.822285Bruxelles-CapitaleBruxelles
111050Ixelles0mixed use building520000.042000000006920to renovate4.38157150.822285Bruxelles-CapitaleBruxelles
231050Ixelles0house599000.04160101015510020to be done up4.38157150.822285Bruxelles-CapitaleBruxelles
341050Ixelles0house599000.031601011516013020good4.38157150.822285Bruxelles-CapitaleBruxelles
451050Ixelles0house575000.031710000004620just renovated4.38157150.822285Bruxelles-CapitaleBruxelles
561050Ixelles0house590000.04225001000020to renovate4.38157150.822285Bruxelles-CapitaleBruxelles
671050Ixelles0house575000.04209100000020None4.38157150.822285Bruxelles-CapitaleBruxelles
781050Ixelles0other property595000.0119511101061740as new4.38157150.822285Bruxelles-CapitaleBruxelles
891050Ixelles0house595777.042500000007020None4.38157150.822285Bruxelles-CapitaleBruxelles
9111050Ixelles0house650000.062501000006020good4.38157150.822285Bruxelles-CapitaleBruxelles

Last rows

Unnamed: 0postal_codecity_nametype_of_propertysubtype_of_propertypricenumber_of_roomshouse_areafully_equipped_kitchenopen_fireterraceterrace_areagardengarden_areasurface_of_the_landnumber_of_facadesswimming_poolstate_of_the_buildinglattitudelongitudeprovinceregion
41523520634342Hognoul0house399000.0418011125157068030as new5.45563950.680810LiègeWallonie
41524520644342Hognoul0house425000.033151011241250030None5.45563950.680810LiègeWallonie
41525520657743Obigies0villa390000.04340110011500216440None3.36428150.662055HainautWallonie
41526520673050Oud-Heverlee0house420000.0518500001046500to be done up4.66789750.821768Brabant flamandFlandre
41527520683050Oud-Heverlee0house435000.042341012000030as new4.66789750.821768Brabant flamandFlandre
41528520701472Vieux-Genappe0villa475000.05216110000155041as new4.40150350.629025Brabant wallonWallonie
41529520711472Vieux-Genappe0villa475000.05215101000155001good4.40150350.629025Brabant wallonWallonie
41530520721461Haut-Ittre0villa499000.05275101010156140None4.29647250.648804Brabant wallonWallonie
41531520731761Borchtlombeek0villa495000.0423510001048840None4.13691550.848178Brabant flamandFlandre
41532520753381Kapellen0house485000.032200011900101940good4.96087850.887345Brabant flamandFlandre